Deep brain stimulation (DBS) treats the cardinal symptoms of Parkinson's disease (PD) including bradykinesia, rigidity, and tremor, and the efficacy of DBS is strongly dependent on finding stimulation parameters (frequency, pulse width, voltage) that maximize symptom reduction without causing side effects. However, there are presently few guidelines to inform programmers regarding selection of stimulation parameters. Thus, device programming is an ad-hoc process, with difficulties of time, expense, and patient discomfort, and patients are often deprived of the optimal benefits of stimulation. A major obstacle to developing rational methods of programming deep brain stimulation parameters for PD is the lack of understanding of the temporal evolution of the motor symptom response to changes in stimulation condition. While tremor responds immediately, bradykinesia and rigidity have delayed responses to changes in stimulation. The delayed responses result in carry-over effects during programming and complicate selection of effective stimulation parameters. The long-term goal of this research is to develop automated methods for programming optimal stimulation parameters. The immediate goals of this proposal are to quantify the temporal changes in symptoms when DBS is turned ON or OFF, and develop an appropriate protocol to measure the effects of stimulation parameter variations on motor symptoms. In aim 1, motor symptoms will be quantified at regular intervals after DBS is turned ON and OFF in human subject volunteers. In aim 2, the motor symptom response data will be fit with mathematical models to quantify the time constants of symptom change and to determine whether the temporal changes depend on the stimulation duration and/or parameters. The results of aims 1 and 2 will be used in aim 3 to design a protocol to measure the steady-state response to stimulation at short times after stimulation is turned ON. The outcome will be an understanding of how the symptoms change over time after stimulation is turned ON and OFF. This will enable the design of programming methods which minimize carry-over effects and the selection of optimal DBS parameters. Optimal stimulation parameters will maximize symptom relief, allow patients to reduce their dopaminergic medications, maximize battery life, and minimize both electrical and drug- related side effects. PUBLIC HEALTH RELEVANCE: Successful treatment of the disabling motor symptoms of Parkinson's disease with deep brain stimulation depends on the proper selection of stimulation parameters. The outcome of this research will be an understanding of how the motor symptoms of Parkinson's disease respond over time to deep brain stimulation. Understanding how the symptoms change over time is required to develop methods to select optimal deep brain stimulation parameters.